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国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 特征与分类

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标 题： A New Bearing Fault Diagnosis Method using Envelope based Feature Extraction
基于包络特征提取的新型轴承故障诊断方法
作 者： Canan Tastimur
Mehmet Karakose
Ilhan Aydin
Erhan Akin
关键词： Vibration signal  [振动信号]
Bearing  [ 轴承]
Induction motor  [ 感应电动机]
Envelope  [ 信封]
K-means  [ K均值故障诊断]
Fault diagnosis  [ 分类]
出版年： 2021
会议名： 2021 Innovations in Intelligent Systems and Applications Conference (ASYU 2021)
2021年智能系统与应用创新大会（ASYU 2021）
届 次：
摘 要： The performance of conventional intelligent diagnostic methods depends on the feature extraction of faulty signals. Feature extraction from defective signals requires signal processing techniques, expert knowledge, and human exertion. In this study, a time series based classification technique was developed to detect malfunctions occurring in induction motors. The proposed algorithm only takes the vibration signals as input. The phase space of the vibration signals taken as input is generated according to the time delay and embedding dimension for each motor status. Each motor condition is divided into two classes: faulty and intact. After the envelope transform is enforced into each signal, phase space is obtained. Then the Gaussian mixture model is formed. After with K-means clustering algorithm, the classification has been made up of four classes which are normal, outer race, inner race, and ball. The proposed approach has been tested on normal and defective vibration signals and results with high accuracy have been obtained.

传统智能诊断方法的性能取决于对故障信号的特征提取。从缺陷信号中提取特征需要信号处理技术、专业知识以及人工干预。本研究提出了一种基于时间序列的分类方法，用于检测感应电机的故障。该算法仅以振动信号作为输入。根据每个电机状态的时间延迟和嵌入维数，生成输入振动信号的相空间。将每种电机工况划分为两类：故障和正常。在对每个信号进行包络变换后，得到相空间，进而构建高斯混合模型。通过K均值聚类算法，最终将分类结果分为四个类别：正常、外圈、内圈和滚珠。该方法已在正常和故障振动信号上进行了测试，取得了较高的准确率。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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